Task mining is a technology that captures user interactions on the desktop, such as application usage and action sequences, to reconstruct how business tasks are actually performed. Organizations use it to document processes, find repetitive work and identify automation candidates that system logs cannot reveal.
How task mining works
- Capture. A desktop agent records interaction events while people work: applications, windows, action sequences and durations.
- Normalization. Raw events are cleaned and grouped so the same task performed by different people becomes comparable.
- Pattern analysis. Algorithms find recurring sequences, variants and inefficiencies across users and time.
- Output. The result is process maps, task documentation and a list of automation candidates with frequency and duration data.
- Capture
- Normalization
- Pattern analysis
- Output
Automation candidateSame three steps, every person
Every click and window, as it happens.Raw events become comparable steps.The same sequence, in every person's work.An automation candidate, ready to rank.
Strengths and limits
Task mining's strength is visibility into work that leaves no system logs: the spreadsheet step, the email handoff, the legacy application. Its classic limits are privacy risk when tools capture too much detail, and narrow scope when analysis stops at single tasks instead of end-to-end processes.
The privacy limit is a design choice, not a law of nature. Nodra's approach minimizes data on the device, excludes content such as keystrokes, screenshots and document text in every configuration, and reports aggregated process insights rather than individual task recordings.
When task mining is the right tool
- Work spans many applications and manual steps that ERP logs cannot see
- You need automation candidates with measured frequency and duration
- You want results in weeks without building system integrations
- Legacy or homegrown tools are central to the workflow